v1.1.1 to v1.1.2

---

name: "cudaq-guide"

title: "CUDA-Q Guide"

description: "Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance."

author: "CUDA-Q Team <cuda-quantum@nvidia.com>"

tags: [cuda-quantum, quantum-computing, onboarding, getting-started, authoring, kernels, nvidia]

tools: [Read, Glob, Grep]

license: "Apache-2.0"

compatibility: "Python 3.10+, C++ 20"

metadata:

author: "CUDA-Q Team <cuda-quantum@nvidia.com>"

tags:

- cuda-quantum

- quantum-computing

- onboarding

- getting-started

- nvidia

languages:

- python

- c++

domain: "quantum"

---

# CUDA-Q Guide

## Purpose

Guide users through CUDA-Q installation, basic kernels, GPU simulation targets,

QPU access, built-in applications, multi-GPU execution, and Python

`@cudaq.kernel` authoring. For Qiskit-to-CUDA-Q ports, route to the

`cudaq-importing` skill instead.

## Prerequisites

- Python 3.10+ for Python CUDA-Q workflows.

- CUDA Toolkit and an NVIDIA GPU for GPU-accelerated targets on Linux.

- CPU-only simulation is available through `qpp-cpu`; macOS is CPU-only.

- C++ workflows require Linux or WSL and C++20.

- QPU workflows require provider-specific credentials and accounts.

## Instructions

- Invoke with `/cudaq-guide [argument]`.

- If no argument is given, display the onboarding menu and ask which topic the

user wants.

- Use the routing table below to choose the relevant reference file.

- Read local CUDA-Q documentation files when the answer depends on a specific

CUDA-Q version or backend behavior.

- Do not answer Qiskit porting questions from this skill; use

`cudaq-importing`.

## Routing by Argument

| Argument | Action | Reference |

|---|---|---|

| `install` | Walk through Python or C++ installation and validation. | [references/onboarding.md](references/onboarding.md) |

| `test-program` | Build and run a Bell-state kernel. | [references/onboarding.md](references/onboarding.md) |

| `gpu-sim` | Select GPU, multi-GPU, tensor-network, or CPU targets. | [references/onboarding.md](references/onboarding.md) |

| `qpu` | Guide provider selection and credential-safe QPU setup. | [references/onboarding.md](references/onboarding.md) |

| `applications` | Summarize CUDA-Q application areas and notebooks. | [references/onboarding.md](references/onboarding.md) |

| `parallelize` | Choose `mgpu`, `mqpu`, async dispatch, or distributed observe. | [references/onboarding.md](references/onboarding.md) |

| `author` | Author CUDA-Q Python kernels, select execution APIs, and debug compiler issues. | [references/authoring.md](references/authoring.md) |

| _(none)_ | Print the menu below and ask which topic to explore. | This file |

## Menu

```text

CUDA-Q Getting Started

CUDA-Q is NVIDIA's unified quantum-classical programming model for CPUs, GPUs, and QPUs.

Supports Python and C++. Docs: https://nvidia.github.io/cuda-quantum/latest/

Choose a topic:

/cudaq-guide install Install CUDA-Q

/cudaq-guide test-program Write and run a Bell-state kernel

/cudaq-guide gpu-sim Accelerate simulation on NVIDIA GPUs

/cudaq-guide qpu Connect to real QPU hardware

/cudaq-guide applications Explore what you can build

/cudaq-guide parallelize Run across GPUs or QPUs

/cudaq-guide author Author @cudaq.kernel Python code

```

## Reference Files

- [references/onboarding.md](references/onboarding.md): installation, test

program, GPU targets, QPU providers, application areas, parallelization

modes, examples, and platform troubleshooting.

- [references/authoring.md](references/authoring.md): execution APIs,

kernel-language constraints, silent-failure pitfalls, recurring coding

patterns, resource metrics, debugging, and validation.

## Limitations

- Guidance targets CUDA-Q Python/C++ workflows, with authoring details focused

on decorator-mode Python APIs used in CUDA-Q 0.14 and 0.15.

- GPU and multi-GPU support depends on local CUDA-Q, CUDA Toolkit, driver, MPI,

and hardware availability.

- QPU access and target options are provider-specific and may change; verify

against local docs before giving operational steps.

## Troubleshooting

- **Import error after `pip install cudaq`:** check Python 3.10+ and supported

OS.

- **No GPU detected:** verify CUDA Toolkit and `nvidia-smi`; fall back to

`qpp-cpu`.

- **Kernel compile error:** read [references/authoring.md](references/authoring.md)

and check the restricted kernel-language subset.

- **Version-specific behavior differs:** compare `cudaq.__version__` with the

latest documentation, then review relevant documentation or source changes

when debugging an installed version that is not the latest release.

- **QPU submission fails:** verify provider credentials are set as environment

variables or through a secrets manager, never hardcoded.

- **Documentation lookup fails:** retry transient MCP or repository lookup once,

then fall back to local docs or official CUDA-Q documentation.